While the world races to build the next GPT model, there’s no doubt that LLMs have a place especially for broad, open-ended reasoning.
But in the enterprise, where precision, latency, and cost matter most, I believe the future belongs to purpose-built small language models (SLMs) embedded within agentic systems.

It’s a direction we’ve been building toward at Fegmo and one we believe will define how AI actually gets deployed inside modern enterprises.


1. SLMs + Agents Beat Monoliths
Modular AI agents powered by SLMs can outperform giant LLMs when tasks are scoped, structured, and repeatable.
Each agent gets a role, a context, and a job to do and does it well.

2. Smaller = Smarter (for Enterprise Tasks)
Most enterprise workflows don’t need poetic reasoning. They need speed, determinism, and structure.
SLMs are faster, cheaper, and often more capable for task-specific operations than general-purpose models.

3. Lower Risk, Higher Control
SLMs are easier to audit, deploy privately, and pair with business rules, ideal for use cases where privacy and compliance matter.

4. From Idea to Execution
At Fegmo, we’ve embraced this vision with dozens of small-model agents powering enrichment, validation, tagging, syndication, and more - helping brands move faster without breaking trust or the bank.


I’m curious how others are approaching this too.
What are you trying with small language models in your stack?
Let’s trade notes on what’s working (and what’s not) as this new agentic era unfolds and how it may reshape how we build, sell, and scale in the age of Vibe Commerce.

Abhishek Ojha